MétaCan
Menu
Back to cohort

Waterbird Responses to Hydrological Management of Wetlands Reserve Program Habitats in New York

2006· article· en· W2180604226 on OpenAlexfundno aff
Matthew R. Kaminski, Guy A. Baldassarre, Aaron P. Yetter

Bibliographic record

VenueWildlife Society Bulletin · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersDelta WaterfowlNew York State Department of Environmental Conservation
KeywordsWaterfowlWetlandHabitatEcologyAbundance (ecology)Drawdown (hydrology)Vegetation (pathology)Spring (device)GeographyEnvironmental scienceBiologyGroundwaterAquifer

Abstract

fetched live from OpenAlex

The Wetlands Reserve Program (WRP) has restored nearly 600,000 ha of wetlands in the United States since inception of the program in 1996. However, no research has evaluated postrestoration management of WRP wetlands in relation to waterfowl and waterbird use. Therefore, we conducted an experiment to compare waterfowl and waterbird abundance and diversity between hydrologically managed (i.e., spring-summer drawdown for vegetation regeneration) and nonmanaged WRP wetlands in central New York, USA, in 2004. We surveyed waterfowl and other waterbirds on 5 managed and 5 nonmanaged wetlands over 3 10-week periods (i.e., spring: 7 Mar-15 May; summer: 16 May-24 Jul; autumn: 25 Jul-30 Sep). We detected a total of 36 taxa of these birds across the 3 periods and both types of wetlands but observed 1.4–2.3 times more taxa on managed than on nonmanaged wetlands among periods. Additionally, we recorded 0.8–13.2 times greater relative abundances (n birds/ha of wetland) of waterfowl and other waterbirds on managed than on nonmanaged wetlands during spring through autumn. We recommend regular postrestoration hydrological management of WRP wetlands to regenerate moist-soil and other emergent plants and promote waterfowl and waterbird use of these restored habitats.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.244
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations61
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueWildlife Society BulletinSame topicPeatlands and Wetlands EcologyFrench-language works237,207